An R package for Longitudinal Drift-Diffusion Mixed Models (LDDMM), v0.4.2.
Authors: Giorgio Paulon, Abhra Sarkar
Codes accompanying “Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults” by Paulon, Llanos, Chandrasekaran, Sarkar.
This package implements a novel generic framework for longitudinal functional mixed models that allows automated assessment of an associated predictor’s local time-varying influence. We build on this to develop a novel inverse-Gaussian drift-diffusion mixed model for multi-alternative decision-making processes in longitudinal settings. Our proposed model and associated computational machinery make use of B-spline mixtures, hidden Markov models (HMM) and factorial hidden Markov models (fHMM), locally informed Hamming ball samplers etc. to address statistical challenges.
The main function is LDDMM; please see the following vignette for details, as well as the main article:
Paulon, G., Llanos, F., Chandrasekaran, B., Sarkar, A. (2021). Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning in adults. Journal of the American Statistical Association 116, 1114-1127
The data included in this package was analyzed in:
Roark, C. L., Paulon, G., Sarkar, A., Chandrasekaran, B. (2021). Comparing perceptual category learning across modalities in the same individuals. Psychonomic Bulletin & Review 28, 898-909
and is available here.
To install the package in R, first install the devtools package, and then use the commands
If you are using a Windows machine, you might have to also install and configure Rtools using the following instructions.
The following is a minimal example of a simple model fit. For numerical stability, the unit of measurement should be such that the numerical values of most response times should lie in \([0, 10]\).
# Load libraries
library(RColorBrewer)
library(ggplot2)
library(dplyr)
library(reshape2)
library(latex2exp)
library(lddmm)
theme_set(theme_bw(base_size = 14))
cols <- brewer.pal(9, "Set1")
# Load the data
data('data')
# Descriptive plots
plot_accuracy(data)
plot_RT(data)
# Run the model
hypers <- NULL
hypers$s_sigma_mu <- hypers$s_sigma_b <- 0.1
# Change the number of iterations when running the model
# Here the number is small so that the code can run in less than 1 minute
Niter <- 25
burnin <- 15
thin <- 1
samp_size <- (Niter - burnin) / thin
set.seed(123)
fit <- LDDMM(data = data,
hypers = hypers,
Niter = Niter,
burnin = burnin,
thin = thin)
# Plot the results
plot_post_pars(data, fit, par = 'drift')
plot_post_pars(data, fit, par = 'boundary')
# Compute the WAIC to compare models
compute_WAIC(fit)To extract relevant posterior draws or posterior summaries instead of simply plotting them, one can use the functions extract_post_mean or extract_post_draws. The following auxiliary functions are available by selecting the corresponding argument in the LDDMM() function:
boundaries = "constant": constant boundary parameters over time, \(b_{d,s}^{(i)}(t) = b_{d,s} + u_{d,s}^{(i)}\) using the article notationboundaries = "fixed": fixed boundaries across input predictors, \(b_{d,s}^{(i)}(t) = b_{d}(t) + u^{(i)}_{d}(t)\) using the article notationboundaries = "fixed-constant": fixed and constant boundaries, \(b_{d,s}^{(i)}(t) = b_{d} + u_{d}^{(i)}\) using the article notationFor bug reporting purposes, e-mail Giorgio Paulon (giorgio.paulon@utexas.edu).
Please cite the following publication if you use this package in your research: Paulon, G., Llanos, F., Chandrasekaran, B., Sarkar, A. (2021). Bayesian semiparametric longitudinal drift-diffusion mixed models for tone learning in adults. Journal of the American Statistical Association 116, 1114-1127